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Intersnack IT KG in Düsseldorf seeks a Data Scientist & ML Engineer to turn data into actionable decisions. You will develop predictive, prescriptive, and optimisation models across procurement, manufacturing, and sales, and integrate them into production environments.
You will collaborate with AI architects to deploy LLMs and NLP capabilities, driving business impact at scale. A hybrid setup with remote flexibility is offered.
Data tells stories, and this role is responsible for turning those stories into decisions. As our Data Scientist & ML Engineer, you will develop the predictive, prescriptive, and optimisation models that give Intersnack the analytical foresight to act confidently across procurement, manufacturing, and sales. You will report into the AI Programme and work alongside AI engineers and data engineers to integrate your models into the knowledge and agentic AI frameworks being built across the organisation, combining classical machine learning rigour with the emerging capabilities of large language models and intelligent agents. Intersnack is committed to growing its people as it grows its capabilities, and this role offers a unique vignpoint ??? Actually: "This is an unique" ???.
What We Can Offer You will have the opportunity to work across a wide and commercially meaningful range of modelling challenges, from demand forecasting and process optimisation in manufacturing, to procurement analytics and scenario modelling, with direct access to the business stakeholders whose decisions your models will inform.
This is not a role where models sit in notebooks; your work will be operationalised, monitored, and iterated upon in production environments.
You will collaborate with AI architects and engineers to integrate predictive logic into agentic workflows, giving your models a reach and impact that scales beyond individual use cases.
Dusseldorf is your home base, with flexibility for remote working, and Intersnack's international footprint ensures your models will operate at genuine scale.
You will divide your time between developing new models and improving existing ones, integrating machine learning outputs into agentic and analytical systems, and actively enabling business stakeholders to understand and trust what those models produce. Your work connects the technical rigour of statistical modelling and ML engineering with the commercial intent of a business that wants AI to create real, measurable value.